(1) Field of Invention
The present invention relates to a system for rapid directed area search and, more particularly, to a system for rapid directed area search which utilizes particle swarm optimization and hierarchical representation schemes.
(2) Description of Related Art
Current approaches to advancing the fields of image analysis include improving detection of salient points, creating image registration algorithms that take into account differential motion and parallax, and exhaustive searches for detecting objects of interest. Due to the computational complexity involved, such approaches are not completely suitable for wide-area imagery.
Recent technological advances have enabled rapid acquisition of copious volumes of imagery from air and space borne platforms. However, only a small percent of the terabytes of information that is logged is analyzed by human analysts. Humans are excellent at analyzing images (i.e., finding low frequency targets of interest in large datasets), but are costly, slow, and fatigue easily. Moreover, the data being analyzed typically includes long intervals between interesting regions, which may be better handled by automated systems.
One aspect of image analysis involves directed area search where the goal is to detect an object of interest. Tasks may include, but are not limited to, finding a moving vehicle and locating a commuter plane crash. The search space for such tasks may range from tens to hundreds of miles. Objects of interest typically have very few pixels and are sensed using gigapixel cameras. In current approaches, some of the objects of interest in a goal driven search may not have suitable templates readily available.
The present invention addresses the question of how interesting objects can be rapidly detected and recognized in imagery that may vary in appearance and/or be embedded in a vast variety of background clutter. Human visual search solves this problem, in part, by combining fast bottom-up reflexive attention cues with slower top-down cognitive processing. Biologically inspired bottom-up attention mechanisms are well understood, quite accurate, and of relatively low complexity. Additionally, the computational models of these mechanisms are fairly easy to implement. In contrast, top-down recognition models are not completely understood, have high computational complexity, and do not have accuracy levels that are suitable for real-world applications.
The present invention attempts to achieve scalable human-like visual search processing and accuracy by combining computational models for bottom-up attention mechanisms for focusing on salient regions, with fast and accurate top-down recognition algorithms for detecting changes, new activities, and anomalous objects.